Freelance AI Application Developers in Sindh

Freelance AI Application Developers in SindhFreelance AI Application Developers in Sindh

Projects People

Karachi, Pakistan

UI/UX Designer | Web+App D | Ecm+Amz listing | SMD | SMM

23: Followers

UI/UX Designer | Web+App D | Ecm+Amz listing | SMD | SMM

Cover image for NutriScan AI Mobile App
People often

NutriScan AI Mobile App People often struggle to understand what is actually inside the food and drinks they consume. Reading ingredients, checking sugar levels, identifying harmful additives, and determining whether a product is suitable for specific diets can be confusing and time-consuming. To solve this problem, I designed NutriScan AI, an AI-powered smart food scanner that allows users to scan any food or beverage product and instantly receive detailed nutrition information, health scores, ingredient analysis, allergy warnings, diet compatibility, expiry details, and healthier alternatives. The app helps users make informed and healthier choices in seconds. Figma link: https://www.figma.com/make/Jr3MaOpY19KccS2Sw4yY8E/NutriScan-AI-Mobile-App?t=H3uNB0PMlbARNuhn-20&fullscreen=1 Figma communiity link: https://proxy-modern-52168077.figma.site

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Cover image for Discover More Clients Faster with AI-Powered Lead Finder

Freelance Lead Finder AI Many freelancers struggle to find potential clients because opportunities are scattered across multiple platforms. They often spend hours searching websites, collecting contact information, and trying to identify businesses that actually need their services. This process is time-consuming, repetitive, and makes it difficult to consistently generate leads. To solve this problem, I designed Freelance Lead Finder AI, an AI-powered platform that helps freelancers discover relevant opportunities in any country based on their field of expertise. The platform scans multiple sources, including LinkedIn, Clutch, Crunchbase, AngelList, Google Business, Behance, Dribbble, and GitHub, and automatically gathers important details such as email addresses, phone numbers, website links, and company information. By bringing everything together in one place, the platform saves time, simplifies lead generation, and helps freelancers connect with potential clients more efficiently. Figma Link: https://www.figma.com/make/O0Q59vNt14tfFRUjsttQIY/LeadRadar-AI-Platform?t=ts5nSnurlHlcNrzQ-1 Figma community link: https://froth-chart-33587522.figma.site

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Cover image for Premium Product Poster Design (Poster)
Every

Premium Product Poster Design (Poster) Every product has a story—great design helps tell it. From skincare and beauty to fashion and lifestyle, premium visuals help brands build trust and stand out online. At Fliqo Studio, we design eye-catching product posters that are clean, modern, and aligned with your brand. 📩 Let's create visuals your audience will remember.

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Cover image for Amazon Infographic Design (Poster)
Great infographics

Amazon Infographic Design (Poster) Great infographics turn product features into customer benefits. By using clear visuals, icons, and easy-to-read layouts, you can answer questions before customers even ask them. At Fliqo Studio, we create Amazon infographic designs that communicate value and help products convert. 📩

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AI Application Developer(2)

proKarachi, Pakistan

Top-Rated Independent Design. Develop. Deliver Results

$1k+: Earned

3x: Hired

5.0: Rating

65: Followers

expert

Top-Rated Independent Design. Develop. Deliver Results

Cover image for I joined the Google Stitch

I joined the Google Stitch Challenge on Contra and here's what I built. Meet my app: a Smart Queue Management System because nobody should have to waste their day standing in line. The idea is simple but powerful: Take your token digitally. Track your queue in real-time. Leave home at the right time. Arrive just when your number is called. Zero waiting. Zero stress. I built this entire experience using Google Stitch from the very first screen to the final prototype. Stitch made it incredibly easy to design, iterate, and bring the whole user flow to life streaming layouts, refining UI with AI, and testing interactions all in one place. What would have taken days was done smoothly and efficiently. The app also features map integration and location navigation so users can find the nearest service center and get directions without ever leaving the app. This project is a result of real effort, late nights, and a vision to solve an everyday problem that millions of people face. Stitch gave me the tools I brought the idea. Built for the Google Stitch Challenge on Contra. Here is my Prototype Link https://stitch.withgoogle.com/projects/4614736589084314221 #GoogleStitch #Contra #StitchChallenge #DesignChallenge #UIDesign #MobileDesign #AIDesign #ProductDesign #QueueManagement #AppDesign #PrototypeDesign #GoogleDesign #MadeWithStitch #UXDesign #ProblemSolving #ContraChallenge #SmartApp #MobileUI #DesignWithAI #BuildWithStitch

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Cover image for Custom WayBack Beauty Store Development E-commerce Website

Custom WayBack Beauty Store Development E-commerce Website

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WYRO Watch Brand Identity Showcase

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Cover image for Crafting Timeless Jewelry for Zorelle

Crafting Timeless Jewelry for Zorelle

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AI Application Developer(1)

maxKarachi, Pakistan

Level 3 Certified Glide Expert.

$50k+: Earned

36x: Hired

5.0: Rating

75: Followers

Level 3 Certified Glide Expert.

Cover image for iDispatchHub App Development for Innovative Logistics Group

iDispatchHub App Development for Innovative Logistics Group

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Cover image for iTVA Connect Project Management + CRM Development

iTVA Connect Project Management + CRM Development

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Cover image for Amazon Fulfilment Management App

Amazon Fulfilment Management App

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AI Application Developer(1)

proKarachi, Pakistan

AI SaaS Engineer | Next.js + Node.js | MVPs to Production

1x: Hired

5.0: Rating

12: Followers

AI SaaS Engineer | Next.js + Node.js | MVPs to Production

Cover image for Kismaa Mobile — Engineering a Real-Time Experience

Kismaa Mobile — Engineering a Real-Time Experience

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Cover image for EduPilotPro Mobile — Parent & Student App (iOS + Android)

EduPilotPro Mobile — Parent & Student App (iOS + Android)

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Cover image for EduPilotPro AI Attendance Agent Project

EduPilotPro AI Attendance Agent Project

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Cover image for Development of EduPilotPro AI-Powered School Operating System

Development of EduPilotPro AI-Powered School Operating System

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AI Application Developer(1)

proKarachi, Pakistan

Production-ready AI agents and SaaS built to scale reliably.

$5k+: Earned

2x: Hired

22: Followers

Production-ready AI agents and SaaS built to scale reliably.

Cover image for 🚀 Contra Community - Rate

🚀 Contra Community - Rate my work 🚀 🔥 Mobile Rental Marketplace Platform A rental-tech startup set out to build a seamless mobile experience that connects merchants with customers looking to rent products on a daily basis. 🎉 The Challenge: Building a rental ecosystem that satisfies both merchants and customers required solving a range of technical and user experience challenges. 🎉 Dynamic Daily Rental System: A robust backend architecture was required to handle real-time availability, pricing logic, bookings, and reservation flows. 🎉 Cross-Platform Experience: Both iOS and Android had to deliver equally smooth and reliable experiences for a diverse user base. 🎉 Secure and Seamless Payments: The platform needed a trusted payment system that protects users while keeping the checkout process smooth. 💯 The Outcome: After a focused and collaborative development sprint, we delivered a fully functional rental marketplace app designed for ease, reliability, and scale.

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Cover image for From Instinct to Infrastructure: How

From Instinct to Infrastructure: How Athliq Went From Trainer's Notebook to Production Performance Platform ────────────────────────────────────────── The Person Who Understood the Problem Before Anyone Else Did Marcus had been a strength and conditioning coach for eleven years. He'd worked with professional football squads, Olympic track athletes, and NCAA programs. He knew exactly what was wrong with how performance data was managed, not because someone told him, but because he'd lived inside the problem every day. Every morning, he'd open four browser tabs, a spreadsheet, and a WhatsApp thread just to answer one question: Is this athlete safe to train today? HRV from one app. Sleep data from another. Yesterday's load from a Google Sheet. Wellness check-ins in a form nobody filled out consistently. Injury notes in a physio's personal folder. The picture was always incomplete, not because the data didn't exist, but because no system was designed to assemble it. He wasn't guessing the problem. He was the problem's daily victim. So he built something. ────────────────────────────────────────── The First Version Had Real Value What Marcus and a developer friend put together in six weeks was genuinely useful. A React frontend. Static JSON files simulating the data feeds he wished he had. A morning readiness dashboard showing each athlete's status: green, amber, red. ACWR calculations. A training plan view. A return-to-play protocol tracker. It looked like a real platform. It felt like one. When he demoed it to his performance director, the response was immediate: "This is what we've been trying to build for two years." The prototype surfaced something important. The problem wasn't that nobody had the data. The problem was nobody had designed the right lens to look at it through. Marcus had. His system organized information around the questions coaches actually ask, not the questions software vendors assume they ask. The initial build worked. For one squad. In static conditions. With fake data. And that was exactly the point where it started to matter, and exactly the point where its limitations became unavoidable. ────────────────────────────────────────── What Started Breaking The prototype had no backend. Data was hardcoded. Every "insight" was pre-written. The readiness scores didn't calculate; they were authored. When a second sport scientist saw the demo and said, "Can we plug in our GPS data?" there was no honest answer that didn't involve a complete rebuild. More specifically: The data layer was decorative. The JSON files looked real but required manual authoring for every scenario. There was no pipeline, no ingestion, no validation. Any live deployment would mean the dashboard showed stale or fabricated numbers, which in a performance context is worse than showing nothing at all. The AI insights were static strings. Every "AI-generated" recommendation was hardcoded text. In the prototype, this was fine. It demonstrated the concept. In production, it would mean the same insight appearing for every athlete regardless of their actual state, silently eroding clinician trust. The system had no concept of time. Training load calculations, ACWR ratios, wellness trends, all of these are temporal by definition. The prototype rendered them as snapshots. A real system needed rolling windows, historical comparison, and the ability to detect change over time. There was no role isolation. The role switcher in the UI was cosmetic. A physio and a performance director seeing the same underlying data with a CSS class change was not access control, it was theater. Nothing would survive a real integration. Real wearables return messy, incomplete, delayed data. The system had no error handling, no retry logic, no fallback states. The first real data feed would have broken the UI in ways that were invisible until they were catastrophic. The system was not wrong. It was early. ────────────────────────────────────────── Why They Didn't Just Fix It Themselves Marcus understood sport science. His developer understood React. Neither of them had built a production data system before, and that gap is not a skills deficiency, it is a domain specialization. What they needed was not someone to rewrite their frontend. The frontend was actually good. The visual hierarchy was sharp, the domain terminology was accurate, the workflows reflected how coaches genuinely think. That institutional knowledge was irreplaceable and not to be discarded. What they needed was: • A real-time data layer that could ingest from multiple sources reliably • A calculation engine that could run ACWR, load zone distributions, and readiness scoring against live data • A structured API contract between the front and back end • Authentication and role-based data scoping that actually enforced access boundaries • Observability, so when something silently broke, someone would know The prototype had proven the concept. The job now was to make the concept dependable. ────────────────────────────────────────── What We Actually Did We kept the frontend. Almost entirely. The visual design, the component architecture, the domain-accurate terminology, all of it stayed. We refactored the data layer, not the UI layer. The prototype's greatest strength was its UX fidelity to how coaches actually work, and we had no interest in rebuilding that from scratch. We built a real data ingestion pipeline. Rather than static JSON, we designed a service layer that could ingest from GPS units, HRV monitors, and wellness form submissions. Each source had its own adapter with validation, normalization, and error handling. Partial data was acceptable; silently wrong data was not. We replaced static calculations with a live computation engine. ACWR calculations now ran against a rolling 28-day window of actual load data. Readiness scoring pulled from real HRV baselines, not fixed numbers, and recalibrated as an athlete's personal baseline shifted over a training block. We replaced pre-written AI insights with a rules-based inference layer. Every insight shown in the platform now corresponded to a condition that was evaluated against live data. If Lena Vasquez's ACWR crossed 1.3 and her HRV dropped more than 15% from her 7-day baseline, the injury risk flag was triggered, not because it was hardcoded, but because those conditions were true. We implemented real role-based access control. A physio sees medical data, injury history, and RTP protocols. A sport scientist sees load analytics and benchmarks. A performance director sees the squad-level overview. A head coach sees readiness and today's session. The frontend already had role-switching built in, we gave it actual enforcement. We added observability throughout. Every data ingestion event was logged. Every calculation that produced an out-of-range result generated an alert. If a data source stopped sending, the system surfaced a staleness warning rather than silently displaying old numbers as current. ────────────────────────────────────────── Tech Stack React 19 + Vite, Tailwind CSS v3, Recharts, React Router v7, Node.js + Fastify, PostgreSQL + TimescaleDB, Redis, GPS/HRV/sleep data adapters (Catapult, Polar, Garmin), Google Forms/Typeform ingestion, Auth0, row-level security, Sentry, Datadog, Railway/Render, Vercel, Supabase Storage What Changed The morning workflow Marcus had been running across four tabs and a spreadsheet now ran in a single view that was populated automatically before he arrived at the training ground. The difference wasn't just convenience. It was confidence. When the dashboard flagged an athlete as high-risk, the coaching staff could interrogate why and trust the answer. When a return-to-play progression showed 80% completion, that number reflected actual criteria met against measured data, not a manually updated percentage. The platform was no longer a demo tool. It was a clinical decision-support system. For the squads using it, the shift was measurable: fewer reactive injury responses, more consistent load monitoring, and perhaps most importantly, a shared language between coaching staff, physios, and sport scientists built around the same data rather than competing interpretations of separate sources. ────────────────────────────────────────── The Part That Rarely Gets Said Most platforms built in this space start from the software side. Someone builds a data collection tool, adds a dashboard, and then tries to reverse-engineer what coaches actually care about. Athliq started from the other direction. A domain expert who understood the problem at a professional level built the frame first, and built it correctly. The pain points were real, the workflows were accurate, the terminology was precise. The engineering work didn't fix a bad idea. It made a good idea survivable. That distinction matters more than most technical case studies acknowledge. The hardest part of building a platform like this is not the infrastructure. It's knowing which questions to answer. That knowledge was already there. Our job was to make sure the system could keep answering them reliably, at scale, over time. ────────────────────────────────────────── Athliq is now in active deployment across two professional squads and one university performance program. The morning readiness dashboard processes real-time data from four integrated sources and serves role-scoped views to performance directors, sport scientists, physiotherapists, and athletes.

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Cover image for KovaRisk: When the Interface Knew

KovaRisk: When the Interface Knew More Than the System ────────────────────────────────────────── The Expert in the Room Compliance officers don't struggle to understand risk. They struggle to act on it fast enough. The team behind KovaRisk understood this precisely. They had spent years inside financial institutions watching the same dysfunction repeat: alerts buried in spreadsheets, investigations tracked in email threads, audit trails reconstructed after the fact. They knew what the interface needed to feel like because they'd lived with the one that didn't. So they built it. Fast. Exactly as they'd imagined it. What emerged was sharp: a risk monitoring dashboard with filterable alert feeds, entity profiles with 12-month risk trajectories, a rule engine with toggle controls, and an audit log that felt immutable. The scenario switcher let compliance teams stress-test different alert load states. The side panel made investigations feel contained and intentional. It looked like a system that had survived production. It hadn't been asked to yet. ────────────────────────────────────────── What Existed Was a Strong Interface - Not a System Every alert in KovaRisk was generated at startup. Every risk score was computed by a random seed function. Every status change - Investigating, Resolved, Escalated - lived in component state. Every timeline event was appended to an in-memory array. Every rule toggle disappeared on refresh. The audit log recorded nothing. The export downloaded a snapshot of what React was holding at that moment. The entity risk history was a curve drawn from a formula, not a record. The logic was there. But it had nowhere to live. A compliance officer investigating a high-risk wire transfer would open the side panel, read the plain-English rule explanation, mark the alert as Investigating, add an internal note - and lose every one of those actions the moment they refreshed the browser. No colleague could see what they'd done. No regulator could verify it had happened. In financial compliance, that's not a UX problem. It's a liability. The prototype validated the workflow brilliantly. It exposed exactly how a compliance team would move through their day. But three things were missing: a source of truth, a coordination layer, and a trail that could be audited under pressure. ────────────────────────────────────────── They Didn't Need More Features - They Needed a System Behind the Interface The team came with a clear idea and a working prototype. What they needed was the architecture that made the prototype a product - the layer that turned interface actions into durable facts. Not a rebuild. A foundation. ────────────────────────────────────────── The Layer That Made It Dependable Data Models: Giving State a Home The first thing to reconstruct was where the data should actually live. KovaRisk's frontend implied a clear schema - alerts, entities, rules, audit events - but none of it persisted. The production system needed a PostgreSQL core with five primary entities: • Alert — with foreign keys to Entity, Rule, Transaction, and a JSONB timeline column for ordered event history • Entity — with risk tier, jurisdiction metadata, and a one-to-many relationship to RiskScore snapshots • Rule — with active/disabled state, trigger thresholds, false-positive tracking, and a versioning mechanism so changes to rules didn't retroactively alter historical alerts • AuditEvent — append-only, with actor ID, action type, target reference, and a server-generated timestamp that clients cannot modify • InternalNote — owned by an alert, with authorship and a soft-delete flag to preserve compliance integrity Every status change, note, escalation, and flag the UI handled ephemerally became a write to this schema. The Alert Generation Engine: Replacing the Seed Function In the prototype, 85 alerts appeared because a loop ran 85 times at startup. In production, alerts are the output of a Transaction Monitoring Service - a background process that runs continuously against incoming transaction streams. This service: • Evaluates each transaction against every active Rule definition • Computes a risk score using rule weights, entity risk tier, jurisdiction flags, and behavioral baselines • Creates an Alert record only when a threshold is breached • Emits an event to a notification queue for high-risk triggers The rule engine the UI let users toggle wasn't decorative. Each rule mapped to an evaluation function in the monitoring service. Disabling a rule didn't just grey out a card - it removed it from the active evaluation set. Re-enabling it didn't retroactively generate alerts it would have caught; it resumed from the point of activation. That distinction mattered for regulatory defensibility. ────────────────────────────────────────── Async Workflows: The Operations the UI Implied But Couldn't Sustain Several interactions in the prototype implied workflows that couldn't complete synchronously. Escalation - When an alert was escalated, the UI changed a status badge. In production, escalation triggers a queue job that: notifies the senior compliance officer via a configured channel, creates a case record linking the alert, and starts a response SLA timer. The UI reflects the outcome - it doesn't produce it. Scheduled Screening - The Sanctions Screening Match rule in the prototype was static. In production, it's a nightly job that re-screens all active entities against updated OFAC, EU, and UN sanctions lists - generating new alerts if a previously clean entity now appears. The results feed back into the alert pipeline. Report Export - The dashboard's Export Report button downloaded a text file of whatever React was holding in memory. In production, report generation is an async job: the user requests the report, the job runs server-side against the live database, and a download link is returned when ready. The content is a verifiable, timestamped record - not a UI snapshot. ────────────────────────────────────────── The Audit Log: From Feed to Fact The prototype's audit log was populated by a generateAuditLog function. It looked comprehensive and immutable. It was neither. Production audit events are written by the API layer on every state-modifying operation - before the response is returned to the client. The table is append-only. No update operations are permitted on AuditEvent records. Timestamps are server-generated in UTC and stored with full precision. Actor identity comes from the authenticated session, not from a string the client sends. The audit log the interface displayed was a simulation of accountability. The production version is the accountability. ────────────────────────────────────────── Tech Stack 1. Frontend: React + Vite, React Router v6, Recharts, React Context + local state, TanStack Query 2. Backend: Node.js + TypeScript, Fastify, Prisma, PostgreSQL, Redis, BullMQ, Passport.js + express-session 3. Infrastructure: AWS ECS / Railway / Render, S3, AWS Secrets Manager / Doppler, Sentry + Datadog, GitHub Actions 4. External Integrations: OFAC / ComplyAdvantage, Refinitiv World-Check, SendGrid / SMTP, Webhooks ────────────────────────────────────────── From Interface to System The prototype answered the right questions. It proved the workflow was sound, the information hierarchy was correct, and the alert investigation pattern worked the way compliance officers needed it to. What it couldn't answer was: what happens when two investigators open the same alert simultaneously? What happens when a rule change needs to take effect immediately across 200 pending alerts? What happens when a regulator asks for every action taken on a specific entity over the past 18 months? Those questions don't live in the interface. They live in the system. KovaRisk's interface was always strong. What it needed was the architecture to make it real - persistent, coordinated, auditable, and defensible under scrutiny. The logic existed from the beginning. We gave it somewhere to live.

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Cover image for Case Study: From Prototype to

Case Study: From Prototype to Production — Building a Credit Management Platform for Ad Operations The Starting Point The founder wasn't guessing the problem. They had spent years inside the ad-credit ecosystem, managing wallet balances across regions, reconciling top-ups via bank wire and stablecoin, chasing compliance documents, and watching campaigns stall because a pixel stopped firing at 2am. They knew exactly what a credit management platform needed to look like. So they built one. Using Figma wireframes translated directly into a React frontend, they shipped a working prototype in days. Two portals were created. One for clients managing wallets and ad accounts, and one for internal operators handling treasury, compliance, and risk. It included real-time spend charts, AI-driven anomaly detection banners, a work queue for ops teams, and an embedded AI assistant that could answer questions about balances, compliance status, and pixel health. The prototype proved the concept. Clients could see their wallet balances, request top-ups, allocate funds to ad accounts, and track campaign performance. Admins could manage treasury operations, review KYB pipelines, monitor risk scores, and process a prioritized work queue. The domain logic was sound. The UX was sharp. But the system was never designed to survive real load. What Started Breaking The prototype worked on mock data. Every API call returned hardcoded arrays after a random delay. There was no backend. No database. No real authentication. The login screen accepted any email and assigned a role based on a toggle switch. Session state lived in localStorage as a raw JSON blob. This was fine for demos. It stopped being fine the moment real money entered the picture. The specific fractures: 1. No transactional integrity. Wallet balances, top-ups, fund transfers, and ad account allocations were all simulated. In a live system, a transfer of $25,000 from a master wallet to a regional sub-wallet is not a UI state change. It is a financial transaction that requires atomicity, audit trails, and rollback capability. The prototype had none of this. 2. Compliance was cosmetic. KYB document statuses were static labels. In production, document verification involves third-party identity providers, expiration tracking, automated re-upload reminders, and regulatory audit logs. The prototype rendered badges like "Verified", "Pending", and "Missing", but nothing enforced the state machine behind them. 3. Risk scoring was decorative. The AI risk badges showed tooltips like "Large P2P transfer detected, document expiring", but these were string literals, not outputs from a scoring model. Real risk assessment requires transaction pattern analysis, velocity checks, cross-referencing compliance status, and escalation workflows that route to the right ops agent. 4. The work queue had no backend. Urgent items like "$45,000 P2P transfer anomaly" appeared in the queue, but resolving them was just a frontend state toggle. There was no case history, no assignment logic, no SLA tracking, and no integration with the compliance or treasury systems that actually needed to act on these events. 5. Multi-tenancy was absent. The platform served one mock client and one mock admin. Scaling to dozens of clients, each with their own wallets, sub-wallets, ad accounts, compliance profiles, and credit limits, required data isolation, permissioning, and tenant-aware queries that did not exist. 6. Ad platform integration was faked. TikTok metrics like impressions, clicks, ROAS, and pixel health were all static datasets. Production requires OAuth-based API integrations, rate-limited data syncing, webhook listeners for pixel status changes, and graceful degradation when platform APIs go down. The founder understood all of this. The prototype was never meant to be the product. It was meant to prove that the product was worth building. Why They Brought In a Team The gap between the prototype and production was not a matter of fixing bugs. It was an architecture problem. The founder needed: - A real backend with transactional guarantees for financial operations - A compliance engine that could enforce document workflows across jurisdictions - A risk system that could ingest transaction data and surface actionable alerts, not static strings - Multi-tenant data architecture with proper isolation and access control - Ad platform integrations that could handle real API contracts, rate limits, and failures - Observability including logging, monitoring, and alerting so the ops team could trust the system under load They did not need someone to rewrite the frontend. They needed someone to build the system underneath it. What We Delivered Financial Operations Layer We replaced the mock API with a transactional backend. Every wallet operation, including top-ups, transfers, allocations, and refunds, now runs through an auditable pipeline with: - Atomic balance updates with optimistic locking - Double-entry ledger for every fund movement - Idempotent transaction processing to prevent duplicate charges - Full audit trail with actor, timestamp, and before and after state Top-up requests now flow through a verification pipeline with submission, proof-of-payment upload, approval, and balance credit. Each step is recorded and reversible. Compliance and KYB Engine - We built a document lifecycle system that replaces static badges with enforced state transitions: - Documents move through missing → uploaded → under_review → verified → expired with rules governing each transition - Expiration monitoring triggers automated client notifications before deadlines - Third-party identity verification integration for director ID matching - Jurisdiction-aware requirements where different regions require different document sets - Audit-grade logging for every status change, reviewer action, and override The KYB pipeline now routes cases to specific compliance agents based on workload, region, and verification type. Risk and Anomaly Detection We replaced hardcoded risk labels with a scoring system that evaluates: - Transaction velocity based on spend acceleration versus historical baseline - P2P transfer patterns and threshold breaches - Compliance status correlation, such as expired documents combined with high spend - Account dormancy detection where inactivity triggers review Risk scores update in near real time. High-risk events automatically generate work queue items with priority, context, and suggested actions. Work Queue and Case Management We transformed the frontend-only task list into an event-driven operations system: - Events from treasury, compliance, and risk systems automatically create queue items - Assignment logic routes items to the right agent based on type, region, and capacity - SLA tracking with escalation rules for overdue items - Case history that records every action, note, and resolution - Enforced status transitions so items cannot be resolved without required actions Multi-Tenant Architecture We designed the data layer for tenant isolation from the ground up: - Each client organization has isolated wallets, documents, ad accounts, and transaction histories - Role-based access control separates client-facing and admin-facing data - API endpoints are tenant-scoped to prevent cross-tenant data leakage - Admin views aggregate across tenants with proper permissioning Ad Platform Integration We built a sync layer for TikTok Ads with an architecture that supports additional platforms: - OAuth-based account linking with token refresh management - Scheduled metric pulls with rate limit awareness and backoff - Pixel health monitoring via event signal tracking instead of static labels - Graceful degradation where stale data is clearly labeled if APIs fail Observability We added the infrastructure the ops team needs to trust the system: - Structured logging for every financial operation, compliance action, and API call - Health dashboards for backend services, integration sync status, and queue throughput - Alerting on anomalies such as failed transactions, sync delays, and SLA breaches - Error tracking with business context, not just stack traces The Outcome The system went from a prototype that could demo well to a platform that could process real money, enforce real compliance, and surface real risk under real load. What changed: - Financial operations now run with transactional guarantees. Wallet balances are accurate, auditable, and reconcilable. - Compliance is enforced, not displayed. Document workflows follow regulated state machines with full audit trails. - Risk detection is continuous and contextual. The ops team acts on scored alerts instead of static labels. - The work queue drives operations. Events flow in automatically, route correctly, and track resolution against SLAs. - Multi-tenancy works. New clients onboard into isolated environments without architectural changes. - Ad integrations sync reliably. When they fail, the system clearly shows it instead of masking stale data. The founder’s instinct was right from the start. The domain model, the UX, and the operational workflows all held up. What we built was the engineering foundation that made it dependable. The system worked until it didn’t. Not because the idea was flawed, but because it was never designed to handle this level of complexity. The prototype proved the product. The production system proved it could scale. Tech Stack Overview 1. Backend: Node.js (NestJS) 2. API: GraphQL 3. Auth: Auth0 4. Database: PostgreSQL 5. Cache/Queue: Redis 6. Search: Elasticsearch 7. Infrastructure: AWS 8. Containers: ECS Fargate 9. CI/CD: GitHub Actions 10. IaC: Terraform 11. Integrations: Stripe 12. Risk Engine: Python (FastAPI) 13. ML Pipeline: scikit-learn 14. AI Assistant: OpenAI GPT-4 15. Vector Store: Pinecone

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AI Application Developer(1)

Karachi, Pakistan

Art Director | UI/UX & Product Strategist | Motion & Brand

5.0: Rating

5: Followers

Art Director | UI/UX & Product Strategist | Motion & Brand

GOTeach.ai — Brand Manual & Visual Identity System

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WACA ANZPCC 2024 — Interactive Brand Ecosystem

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Cover image for Josef's: 360° Visual Identity & Social Motion

Josef's: 360° Visual Identity & Social Motion

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Cover image for ROLLS RICE Sushi | High-Fidelity Asian Fusion Branding

ROLLS RICE Sushi | High-Fidelity Asian Fusion Branding

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AI Application Developer(1)

Karachi, Pakistan

Web & App Developer & Designer | Webflow, Framer, React

17: Followers

Web & App Developer & Designer | Webflow, Framer, React

Cover image for This project began as an experiment: What if property discov...

This project began as an experiment: What if property discovery could feel as intuitive as browsing your favorite apps? Using Figma for the UX, Lovable AI to accelerate development, and Claude.ai (http://Claude.ai) for intelligent automation, I brought this concept to life. With Supabase powering the backend, the platform delivers real-time property data, authentication, and an all-around smooth experience. The final result is a sleek real-estate application that blends modern design with AI-driven recommendations. https://ismailsajid.my.canva.site/luxeestate-website-by-ismail-sajid , https://lovely-lollipop-fc1036.netlify.app/

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Cover image for From concept to conversion 👇 A clean, premium experience bu...

From concept to conversion 👇 A clean, premium experience built to showcase quality, professionalism, and effortless booking for a luxury auto detailing brand. #buildinpublic #contracondensed #uiux

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Cover image for ✨ Imagine your salon never missing a call again. I designed ...

✨ Imagine your salon never missing a call again. I designed an Aura AI receptionist for salons that books appointments, answers FAQs, and responds 24/7, so stylists focus on clients, not the phone. Would this help your salon? 💬👇

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Cover image for Where pets feel safe, calm, and truly cared for 🩺🐕 This is...

Where pets feel safe, calm, and truly cared for 🩺🐕 This is what modern veterinary care should look like. Agree? 👇

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AI Application Developer(1)

Karachi, Pakistan

Full Stack Developer | PHP, MySQL & JavaScript

Full Stack Developer | PHP, MySQL & JavaScript

Cover image for WinClient AI – AI-Powered Freelancing

WinClient AI – AI-Powered Freelancing Assistant WinClient AI is a full-stack web application designed to help freelancers work smarter, find better opportunities, and manage their business from one powerful platform. The platform combines AI-powered tools with business management features to simplify the freelancing workflow. It helps users discover relevant jobs, optimize their profiles, analyze job opportunities, and manage projects more efficiently through a modern and intuitive dashboard. Key Features 🤖 AI-powered Upwork Profile Optimization 🎯 Fiverr Profile & Gig Optimization 📢 Real-Time Upwork Job Alerts 📊 AI Job Match Analysis 👨‍💼 Complete Admin Dashboard 📁 Business & Client Management 📈 Project & Proposal Management 🔐 Secure User Authentication ⚡ Auto Bid System (Currently in Development) Tech Stack PHP MySQL JavaScript HTML5 CSS3 Bootstrap jQuery My Role I independently designed and developed the complete application, including the user interface, backend logic, database architecture, authentication system, admin dashboard, and AI-powered workflow features. 🚀 This project is currently under active development. A live demo will be available soon, and I will provide the hosted project link once deployment is completed.

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Cover image for 🚀 Excited to Share a

🚀 Excited to Share a New Milestone! This Monday, I'll be presenting the 3rd Review of WinClient AI, the project I'm building for the Aptech Vision Competition 2026. 🎯 WinClient AI is an AI-powered platform built to help freelancers and agencies work smarter, save time, and win more clients by automating the most important parts of their freelance workflow. 🔥 Core Features of WinClient AI ✅ AI Proposal Generator – Generate personalized, high-converting proposals in seconds. ✅ Profile Analyzer & Optimizer – Improve your Upwork and Fiverr profiles with AI-powered recommendations. ✅ Real-Time Upwork Job Alerts – Instantly receive relevant job opportunities based on your skills. ✅ Smart Job Match – AI analyzes your profile and recommends whether you should apply for a job or skip it. ✅ Gig & Catalog Optimizer – Optimize Fiverr Gigs and Upwork Catalogs for better visibility. ✅ AI Bid Strategy – Paste a job URL and get the recommended bid amount, connects to use, and winning strategy. ✅ Fake Job Detector – Identify low-quality or suspicious job posts before wasting your connects. ✅ AI Content Manager – Get content ideas, social media post suggestions, and discover the latest trends in your industry. Every review helps me improve this platform and brings me one step closer to creating a powerful AI solution for freelancers around the world. I'm continuously learning, building, testing, and refining WinClient AI based on real freelance workflows. Your feedback and support mean a lot. 💙 If you're a freelancer, agency owner, or AI enthusiast, I'd love to hear your thoughts in the comments!

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Cover image for 🚀 Building WinClient AI —

🚀 Building WinClient AI — My Journey Every freelancer knows that finding the right projects, writing proposals, and managing clients can take a lot of time. That's why I'm building WinClient AI — an AI-powered platform designed to help freelancers work smarter, save time, and grow their business. Current Features: ✅ AI-Powered Upwork Profile Optimization ✅ Fiverr Profile & Gig Optimization ✅ Real-Time Upwork Job Alerts ✅ AI Job Match Analysis ✅ Business & Client Management ✅ Secure Admin Dashboard Coming Soon 🚀 Auto Bid System This project is helping me improve my skills in Full Stack Web Development, backend architecture, database design, and building AI-powered business applications. Tech Stack 💻 PHP 🗄️ MySQL ⚡ JavaScript 🎨 HTML5 & CSS3 🧩 Bootstrap I'm continuously learning, building, and improving. The live demo will be available after deployment, and I'll share it once it's ready. I'd love to hear your feedback and suggestions! If you were a freelancer, which feature would help you the most: AI Job Matching, Profile Optimization, or Auto Bid? Let me know in the comments!

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Cover image for Courier Management System – Full

Courier Management System – Full Stack PHP & MySQL Web Application I designed and developed a complete Courier Management System to simplify shipment management, parcel tracking, and delivery operations. The system provides a secure and user-friendly experience through role-based access for administrators, staff, agents, delivery personnel, and customers. Key Features 📦 Shipment & Parcel Management 🚚 Real-Time Courier Tracking 👥 Role-Based User Management 🔐 Secure Authentication & Email Verification 📊 Admin Dashboard & Reports 📝 Customer Registration & Order Management 📱 Responsive Design for All Devices Tech Stack PHP MySQL JavaScript HTML5 CSS3 Bootstrap jQuery My Role I independently designed the user interface, developed the backend, integrated the MySQL database, implemented authentication, and built the complete business workflow for courier management. This project demonstrates my ability to build secure, scalable, and business-focused web applications using modern full-stack web development technologies.

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AI Application Developer(1)